B = TreeBagger(NTrees,X,Y)
B = TreeBagger(NTrees,X,Y,'param1',val1,'param2',val2,...)
B = TreeBagger(NTrees,X,Y) creates an ensemble B of NTrees decision trees for predicting response Y as a function of predictors X. By default TreeBagger builds an ensemble of classification trees. The function can build an ensemble of regression trees by setting the optional input argument 'method' to 'regression'.
X is a numeric matrix of training data. Each row represents an observation and each column represents a predictor or feature. Y is an array of true class labels for classification or numeric function values for regression. True class labels can be a numeric vector, character matrix, vector cell array of strings or categorical vector. TreeBagger converts labels to a cell array of strings for classification.
For more information on grouping variables, see Grouping Variables.
B = TreeBagger(NTrees,X,Y,'param1',val1,'param2',val2,...) specifies optional parameter name/value pairs:
|'FBoot'||Fraction of input data to sample with replacement from the input data for growing each new tree. Default value is 1.|
Square matrix C, where C(i,j) is the cost of classifying a point into class j if its true class is i. Alternatively, cost can be a structure S having two fields:
The default value is C(i,j) = 1 if i ~= j, and C(i,j) = 0 if i = j.
If Cost is highly skewed, then, for in-bag samples, the software oversamples unique observations from the class that has a large penalty. For smaller sample sizes, this might cause a very low relative frequency of out-of-bag observations from the class that has a large penalty. Therefore, the estimated out-of-bag error is highly variable, and might be difficult to interpret.
|'SampleWithReplacement'||'on' to sample with replacement or 'off' to sample without replacement. If you sample without replacement, you need to set 'FBoot' to a value less than one. Default is 'on'.|
|'OOBPred'||'on' to store info on what observations are out of bag for each tree. This info can be used by oobPredict to compute the predicted class probabilities for each tree in the ensemble. Default is 'off'.|
|'OOBVarImp'||'on' to store out-of-bag estimates of feature importance in the ensemble. Default is 'off'. Specifying 'on' also sets the 'OOBPred' value to 'on'.|
|'Method'||Either 'classification' or 'regression'. Regression requires a numeric Y.|
|'NVarToSample'||Number of variables to select at random for each decision split. Default is the square root of the number of variables for classification and one third of the number of variables for regression. Valid values are 'all' or a positive integer. Setting this argument to any valid value but 'all' invokes Breiman's 'random forest' algorithm.|
|'NPrint'||Number of training cycles (grown trees) after which TreeBagger displays a diagnostic message showing training progress. Default is no diagnostic messages.|
|'MinLeaf'||Minimum number of observations per tree leaf. Default is 1 for classification and 5 for regression.|
|'Options'||A structure that specifies options that govern the computation
when growing the ensemble of decision trees. One option requests that
the computation of decision trees on multiple bootstrap replicates
uses multiple processors, if the Parallel Computing Toolbox™ is
available. Two options specify the random number streams to use in
selecting bootstrap replicates. You can create this argument with
a call to statset. You can retrieve values of the
individual fields with a call to statget.
Applicable statset parameters
Prior probabilities for each class. Specify as one of:
If you set values for both Weights and Prior, the weights are renormalized to add up to the value of the prior probability in the respective class.
If Prior is highly skewed, then, for in-bag samples, the software oversamples unique observations from the class that has a large prior probability. For smaller sample sizes, this might cause a very low relative frequency of out-of-bag observations from the class that has a large prior probability. Therefore, the estimated out-of-bag error is highly variable, and might be difficult to interpret.
Categorical predictors list, specified as the comma-separated pair consisting of 'CategoricalPredictors' and one of the following.
In addition to the optional arguments above, this method accepts all optional fitctree and fitrtree arguments with the exception of 'minparent'. Refer to the documentation for fitctree and fitrtree for more detail.
Load Fisher's iris data set.
Train a bagged ensemble of classification trees using the data and specifying 50 weak learners. Store which observations are out of bag for each tree.
rng(1); % For reproducibility BaggedEnsemble = TreeBagger(50,meas,species,'OOBPred','On')
BaggedEnsemble = TreeBagger Ensemble with 50 bagged decision trees: Training X: [150x4] Training Y: [150x1] Method: classification Nvars: 4 NVarToSample: 2 MinLeaf: 1 FBoot: 1 SampleWithReplacement: 1 ComputeOOBPrediction: 1 ComputeOOBVarImp: 0 Proximity:  ClassNames: 'setosa' 'versicolor' 'virginica'
BaggedEnsemble is a TreeBagger ensemble. BaggedEnsemble.OOBIndices stores the out-of-bag indices as a matrix of logical values.
Plot the out-of-bag error over the number of grown classification trees.
oobErrorBaggedEnsemble = oobError(BaggedEnsemble); plot(oobErrorBaggedEnsemble) xlabel 'Number of grown trees'; ylabel 'Out-of-bag classification error';
The out-of-bag error decreases with the number of grown trees.
To label out-of-bag observations, pass BaggedEnsemble to oobPredict.
TreeBagger generates in-bag samples by oversampling classes with large misclassification costs and undersampling classes with small misclassification costs. Consequently, out-of-bag samples have fewer observations from classes with large misclassification costs and more observations from classes with small misclassification costs. If you train a classification ensemble using a small data set and a highly skewed cost matrix, then the number of out-of-bag observations per class might be very low. Therefore, the estimated out-of-bag error might have a large variance and might be difficult to interpret. The same phenomenon can occur for classes with large prior probabilities.
Avoid large estimated out-of-bag error variances by setting a more balanced misclassification cost matrix or a less skewed prior probability vector.